Evidence map›Paper›PMID 36551709›Full record

ArticleCancers2022

CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling.

Ricarda Hinzpeter, Roshini Kulanthaivelu, Andres Kohan, Lisa Avery, Nhu-An Pham, Claudia Ortega, Ur Metser, Masoom Haider, Patrick Veit-Haibach

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 13 citations in OpenAlex.

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  11. Secreted microRNAs in bone metastasis.Journal of bone and mineral metabolism · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Ricarda HinzpeterJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.ORCID 0000-0002-4707-3042
Roshini KulanthaiveluJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
Andres KohanJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
Lisa AveryDepartment of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
Nhu-An PhamPrincess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada.
Claudia OrtegaJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
Ur MetserJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.ORCID 0000-0003-3726-8994
Masoom HaiderJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
Patrick Veit-HaibachJoint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
University Health Network · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42-86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution's web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.

Indexed as

computed tomographypancreatic ductal adenocarcinomaradiogenomicswhole genome sequencing

Identifiers

PMID36551709
PMCPMC9776865
OpenAlexW4312141111

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.